The Application of Artificial Intelligence Technology in the US Civil Court System
Bibliographic record
Abstract
Introduction: in the era of the active introduction of digital technologies, more and more processes are being automated and smart machines are taking over the work of people. Even at the end of the 20th century, automatic spell-checking and search engines were perceived by many as “highly intelligent” information technologies. Currently, such processes have become completely trivial for most people and have given way to more advanced technologies. The intelligent face recognition systems installed in public places and airports allow you to verify a person’s identity, as well as assist in the capture of criminals. The smart assistants in mobile devices, for example, Google Maps, provide additional information about the destination (working hours, the name of the organization). However, there is more and more debate about the introduction of artificial intelligence technologies in the judicial process. Many experts in the field of information and communication technologies, as well as practicing lawyers, argue that thanks to the accumulated experience and judicial practice, it is possible to predict and make court decisions based on certain algorithms for certain categories of cases. This practice already exists in the system of alternative settlement of civil disputes. The first such decision was made by a robot mediator back in 2019 in the High Court of England and Wales. To resolve the dispute, the Smartsettle ONE system developed by the Canadian company iCan Systems was used. The use of artificial intelligence technology allowed resolving the dispute between the parties and coming to an agreement in less than an hour. The legislator approaches the issues of the introduction of artificial intelligence technology in the system of state courts more carefully. However, court cases do not always require a comprehensive individual approach to decision-making and many cases can be processed automatically, at least, partially. In this regard, it seems appropriate to explore in the paper the main opportunities and risks of using artificial intelligence through the example of the civil justice system of the United States of America. The purpose of the study is achieved by answering several questions: how can artificial intelligence be useful for courts? What mechanisms of the justice system need to be improved for the effective operation of artificial intelligence systems? What forms of artificial intelligence exist in the US civil court system? How can courts and judges work with artificial intelligence under the standards of a fair procedure for considering civil disputes? The methodology is based on a theoretical approach to the study of the most commonly used artificial intelligence technologies in the US civil justice system, as well as a number of national laws and other regulations. Based on the analysis of the theoretical data obtained, in the paper, the author analyzes the current trends and mechanisms for resolving civil disputes using artificial intelligence systems and also highlights some related problems. The results of the research can be used in determining the key goals and objectives of a procedural nature, improving the functioning of judicial and non-judicial organizations, law enforcement, research activities, as well as in teaching activities, in particular, during lectures and seminars on courses of private international law and civil procedure. Conclusions: increasing the level of awareness of participants in civil law disputes about current trends and tools for the administration of justice contributes to the development of the institution of civil proceedings, as well as contributes to increasing transparency and increasing the degree of trust of citizens in the judicial system as a whole.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".